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Humans use their dexterous fingers and adaptable palm in various multiobject grasping strategies to efficiently move multiple objects together in various situations. Advanced manipulation skills, such as finger-to-palm translation and palm-to-finger translation, enhance the dexterity in multiobject grasping. These translational movements allow the fingers to transfer the grasped objects to the palm for storage, enabling the fingers to freely perform various pick-and-place tasks while the palm stores multiple objects. However, conventional grippers, although able to handle multiple objects simultaneously, lack this integrated functionality, which combines the palm's storage with the fingers' precise placement. Here, we introduce a gripper for multiobject grasping that applies translational movements of fingertips to leverage the synergistic use of fingers and the palm for enhanced pick-and-place functionality. The proposed gripper consists of four fingers and an adaptive conveyor palm. The fingers sequentially grasp and transfer objects to the palm, where the objects are stored simultaneously, allowing the gripper to move multiple objects at once. Furthermore, by reversing this process, the fingers retrieve the stored objects and place them one by one in the desired position and orientation. A finger design for simple object translating and a palm design for simultaneous object storing were proposed and validated. In addition, the time efficiency and pick-and-place capabilities of the developed gripper were demonstrated. Our work shows the potential of finger translation to enhance functionality and broaden the applicability of multiobject grasping.
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http://dx.doi.org/10.1126/scirobotics.ado3939 | DOI Listing |
IEEE Trans Neural Netw Learn Syst
June 2025
Brain-computer interface (BCI) offers a direct communication and control channel between the human brain and external devices, presenting new pathways for individuals with physical disabilities to operate robotic arms for complex tasks. However, achieving multiobject grasping tasks under low signal-to-noise ratio (SNR) consumer-grade EEG signals is a significant challenge due to the lack of robust decoding algorithms and precise visual tracking methods. This article proposes, ArmBCIsys, an integrated robotic arm system that combines a novel dual-branch frequency-enhanced network (DBFENet) to robustly decode EEG signals under noisy conditions with the high-precision vision-guided grasping module.
View Article and Find Full Text PDFIEEE Trans Neural Syst Rehabil Eng
May 2025
The anthropomorphic grasping capability of prosthetic hands is critical for enhancing user experience and functional efficiency. Existing prosthetic hands relying on brain-computer interfaces (BCI) and electromyography (EMG) face limitations in achieving natural grasping due to insufficient gesture adaptability and intent recognition. While vision systems enhance object perception, they lack dynamic human-like gesture control during grasping.
View Article and Find Full Text PDFSci Adv
April 2025
Department of Mechanical and Aerospace Engineering, University of California San Diego, 9500 Gilman Dr., La Jolla, CA 92093, USA.
Rigid robot arms face a tradeoff between their overall reach distance and how compactly they can be collapsed. However, the tradeoff between long reach and small storage volume can be resolved using deployable structures such as tape springs. We developed bidirectional tape spring "fingers" that have large buckling strength compared to single tape springs and that can be spooled into a compact state or unspooled to manipulate objects.
View Article and Find Full Text PDFSci Robot
December 2024
Biorobotics Laboratory, Soft Robotics Research Center, Institute of Advanced Machines and Design, Department of Mechanical Engineering, Institute of Engineering, Seoul National University, Seoul 08826, Republic of Korea.
Front Robot AI
November 2024
Institute of Robotics and Mechatronics, German Aerospace Center (DLR), Wessling, Germany.
For certain tasks in logistics, especially bin picking and packing, humans resort to a strategy of grasping multiple objects simultaneously, thus reducing picking and transport time. In contrast, robotic systems mainly grasp only one object per picking action, which leads to inefficiencies that could be solved with a smarter gripping hardware and strategies. Development of new manipulators, robotic hands, hybrid or specialized grippers, can already consider such challenges for multi-object grasping in the design stages.
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